New Approaches to Quantify Impacts of Climate Change on Agriculture

Many scientists accept that climate change has a notable impact on U.S. agricultural production. However, estimates differ widely, making it difficult to develop mitigation procedures.

Two agricultural economists at the University of Illinois have keenly observed how the selection of statistical methodology impacts climate study results. The researchers also suggest a more precise and region-specific approach to data analysis.

If you pay attention to forecasts of how the climate will affect U.S. agriculture, the results are completely different. Some scientists predict it’s going to have a positive impact for the nation in the long run, some report it’s going to have a negative impact.

Sandy Dall’Erba, Study Co-Author and Professor, Department of Agricultural and Consumer Economics, University of Illinois

Dall’Erba is also the director of the Center for Climate, Regional, Environmental and Trade Economics (CREATE) at the University of Illinois. Chang Cai, a doctoral student at the Department of Agriculture and Economics (ACE), is the lead author of the study. The researchers accounted for all the academic literature that estimates the impact of climate change on U.S. farmland values and revenues, focusing on every U.S. county.

According to the researchers, the county-level scale, apart from being more precise, it is also key for regional policymakers to draft country-specific decisions in regions where climate change is predicted to make serious challenges.

There is not a single commodity that is produced all over the U.S. The only way we can really understand the relationship between climate and agriculture is that rather than focusing on a particular crop or livestock, we look at economic impacts. Looking at aggregated agricultural outcomes allows us to compare the situation across every county in the U.S.

Sandy Dall’Erba, Study Co-Author and Professor, Department of Agricultural and Consumer Economics, University of Illinois

The researchers analyzed how studies group locations for the analysis and how such grouping influences the results.

“Early studies would assume one additional degree of Celsius or Fahrenheit in Arizona will have the exact same marginal effect on agriculture as one additional degree in Illinois, which makes very little sense because you're looking on the one hand at a place that is quite used to high temperature and low precipitation, versus a place which is used to moderate temperature and much more precipitation,” explained Dall’Erba.

Recently, researchers attempted to differentiate results and estimate effects based on local conditions. A well-known method is to divide the United States into irrigated versus rainfed areas, roughly stretching through a west/east partition along the 100^th meridian. While Arizona and Illinois fall into different groups, Montana and Arizona are expected to experience similar weather effects.

Dall’Erba used another approach in her own research that compares low-versus high-elevation areas, and the third approach grouped locations along the state lines. Dall’Erba states that the researchers employed the latter approach as it was more straightforward and relevant for policy measures.

However, it did not provide very precise results because state lines rarely confirm atmospheric characteristics. Even though all these methods had some merits, they also had disadvantages.

We discovered that results really do differ in terms of what the future impact of climate change will be if you choose one grouping versus another, especially in primary agricultural areas. We also found out that none of those groupings is better than any other in predicting what the future outcome will be.

Chang Cai, Study Lead Author, and Doctoral Student, Department of Agriculture and Economics, University of Illinois

The researchers suggested using one of three new statistical approaches that provide county-specific climate impact estimates. All these approaches are driven by data and begin with predictions on what the groups will look like. However, these approaches quantify the data to find out both the number of groups and who belongs to what group.

These scientific approaches are known as C-Lasso, casual forest algorithm and geographically weighted regression, which are employed for analysis in other fields, including labor market and energy conservation. These methods were not used in climate change studies so far.

“You really let the data speak for themselves; you do not impose anything on your model. As soon as you start making choices on how one should group the observations, you've already guided your results in one direction. And then you’ll want to defend your choice. We’re hoping future researchers will be more cautious about a priori choices,” concluded Dall’Erba.

The researchers are already trying to apply these new approaches to a detailed analysis of climate change and U.S. agricultural production. The study is expected to be presented in a forthcoming paper and to direct the implementation of place-tailored climate change adaption strategies.

Journal Reference:

Cai, C & Dall’Erba, S (2021) On the evaluation of heterogeneous climate change impacts on US agriculture: does group membership matter? Climatic Change. doi.org/10.1007/s10584-021-03154-5.